• Scatter plot
• Histogram
• Bar plot and pie chart
• Boxplot
• qqnorm, qqline, qqplot (distribution comparision)
• ggplot
• Heat Map

a <- c(1, 3, 4)
b <- c(2, 5, 6)
plot(x = a, y = b)

Arguments: x and y are coordinates of points in the plot

plot(x = a, y = b, 
     type = "b",                              # Type of plot: (points, lines, both, none)
     lty = 2, lwd = 2, col = "red",           # Line: (type, width, color) 
     pch = 14,                                # Point: (shape)
     main = "Correlation", sub = "Figure 1",  # Figure: (title; subtitlel)
     xlab = "a value", ylab = "b value",      # x,y axis: (label)
     xlim = c(0,5), ylim = c(-1,10))          # x,y axis: (range)

plot(x = a, y = b, type = "b", lty = 2, lwd = 2, col = "red", pch = 14, 
     main = "Title", sub = "Figure 1. subtitle",  
     xlab = "X name", ylab = "Y name", xlim = c(0,5), ylim = c(-1,10))
# add text 
# x: coordinates, y: coordinates, labels: text, cex: text size
text(x = a[1], y = b[1], labels = "Hello", cex = 2)  

• lines() / legend()

plot(x = c(1,3), y = c(2,4), col = "red", type = "b", xlim = c(0,5), ylim = c(-1,10))
lines(x = c(2,5), y = c(6,4), col = "blue", type = "b", xlim = c(0,5), ylim = c(-1,10)) 

# add legend
legend( x = "topright", col = c("red", "blue"), lty = c(1,1), legend = c("line_1","line_2"))

• seq()

x_v <- seq(from = 0, to = 1, by = 0.05) # by:increment of the sequence.
y_v <- x_v + 3 
plot(x = x_v, y = y_v )

Plot the following two lines:

• y = cos(x): blue, dashed, thick line
• y = sin(x): red, solid, thin line
• Add title, subtitle, legend, functions as below

Draw the figure happily

Remove OLS regression line? (reg.line = FALSE)
Remove non-par smooth line? (smooth = FALSE)

# library(plotly)
# plot_ly(x = iris$Sepal.Length, y = iris$Petal.Length)
library(car)
scatterplot(x = iris$Sepal.Length, y = iris$Petal.Length)

#pairs(x = iris[,1:4])
pairs(formula = ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris)

var <- iris$Sepal.Width
hist(x = var, breaks = 20, col = "gray", freq = F)
lines(density(var))
abline(v = mean(var), col = "red")
abline(v = quantile(var, c(0.025,0.975)), lty = 2, col = "blue")

• Add Math formular and axis note

plot(1, type = "n")
text(x = 1, y = 1, labels = expression(mu), cex = 4)
axis(side = 1, at = 1.1, labels = expression(sigma^2), cex.axis = 2, col.axis = "red")

Plot histogram of variable "numbers"; add density line; mark its 5% and 95% quantile as follows.

set.seed(10)
numbers <- rnorm(n = 100, mean = 0, sd = 1)

par(mfrow = c(1,2)) #layout of multiple figures
barplot(height = c(1,2,3))
pie(x = c(1,3,4), 
    label = c("one", "three","four"), col = c("red","green","pink"))#areas of pie slices

gender <- c("male","female","female","female PhD")
tb_g <- table(gender)

par(mfrow = c(1,2))
boxplot(iris$Sepal.Length)
boxplot(Sepal.Length ~ Species , data = iris, col = c("red","green","pink"))

• Generate random numbers

par(mfrow = c(1,2))
set.seed(1)
a <- rnorm(n = 200, mean = 1, sd = 2) # normal distribution
b <- runif(n = 200, min = 0, max = 1) # uniform distribution
hist(a, col = "blue")
hist(b, col = "green")

• QQ norm

par(mfrow = c(1,2))
a <- rnorm(n = 300, mean = 1, sd = 2)
b <- runif(n = 300, min = 0, max = 1)
qqnorm(a, main = "normal vs normal"); qqline(a)
qqnorm(b, main = "uniform vs normal"); qqline(b)

• QQ plot

par(mfrow = c(1,2))
a <- rnorm(n = 300, mean = 1, sd = 2)
b <- runif(n = 300, min = 0, max = 1)

my_unif <- runif(n = 300, min = 0, max = 1)
qqplot(my_unif,a, main = "myUnif vs normal" )
qqplot(my_unif,b, main = "myUnif vs unif" )

layout(matrix(1:4,2,2), widths = c(1,2), heights = c(2,1))
plot(x = 1:3, y = 1:3, main = "F1")
plot(x = 1:3, y = 1:3, main = "F2")
plot(x = 1:3, y = 1:3, main = "F3")
plot(x = 1:3, y = 1:3, main = "F4")

After the pdf() command, graphs are redirected to file test.pdf.

pdf(file = "test.pdf"); plot(1); dev.off()
#  Works for all common formats similarly: jpeg, png, ps, tiff, ...
jpeg(filename = "test.jpeg"); plot(2); dev.off()
getwd() # check working file path
dir() # show files in current path
setwd() # change working file path
dir.create() # create new folder
file.remove() # remove files

• Plot the figure below and save it as a "myplot.jpeg" file in your desktop

• Plots can be assembled in pieces

library(ggplot2)
baseplot <- ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width))
p1 <- baseplot + geom_point()
p2 <- baseplot + geom_line()
#ggsave("p2.jpeg", p2) 
library(gridExtra) #layout package for ggplots
p_all <- grid.arrange(p1, p2,   ncol = 2, nrow = 1)

• point size

baseplot + geom_point(size = 3)

• point color

baseplot + geom_point(color = "red")

• point color

baseplot + geom_point(aes(color = Species))

• point shape

baseplot + geom_point(shape = 12)

• Point shape & color varied with species

baseplot + geom_point(aes(color = Species, shape = Species))

• histogram: geom_histogram()
• bar plot: geom_bar()

P1 <- ggplot(data = iris, aes(Sepal.Length)) + geom_histogram(color = "blue", fill = "pink")
P2 <- ggplot(data = iris, aes(Sepal.Length)) + geom_density(fill = "lightyellow")
P3 <- ggplot(data = iris, aes(Species)) + geom_bar(fill = "lightgreen")
grid.arrange(P1, P2, P3,  ncol = 3, nrow = 1)

Plots can also have facets to make lattice plots

ggplot(iris, aes(Sepal.Length)) + geom_histogram() + facet_grid(.~ Species )

Use stat_smooth() to add a linear fit

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) + geom_point(color = "red") +
            stat_smooth(method = "lm")

• Color different species

ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species, shape = Species)) +
            geom_point(size = 2, alpha = 0.3) + stat_smooth()

ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) + geom_point() + 
            labs(title = "Iris\nWidth~Length", x = "length", y = "Width") +
            theme_bw() +
            theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
            theme(plot.title = element_text(hjust = 0.5))

library(googleVis)
M1 <- gvisMotionChart(Fruits, idvar = 'Fruit', timevar = 'Year')
plot(M1)

dataPath <- "https://raw.githubusercontent.com/liyujiao1026/r_graph/master/seatData.csv"
seatData <- read.csv(dataPath)
ggplot(seatData, aes(y = y, x = x)) +
            geom_tile(aes(fill = rank))

ggplot(seatData, aes(y=y, x=x)) + geom_tile(aes(fill = rank)) +
            scale_fill_gradient(low = "yellow",high = "red", name = "rank") +
            scale_x_continuous(breaks = c()) +scale_y_continuous(breaks = c()) +
            theme_minimal(base_size = 12) + xlab("") + ylab("") + 
            theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
                  legend.position="bottom",legend.key.height = unit(0.4, "cm")) +
            ggtitle("Heatmap of seat popularity") + 
            theme(plot.title = element_text(hjust = 0.5))